原棉性能与成纱质量的WEKA-人工神经网络预测  

Predication of the effect of cotton fiber property on formed yarn quality by WEKA-artificial neural network

作　　者：李雪月 高晓艳[2] 吴志刚[3] 郁崇文[3] 

机构地区：[1]郑州第一纺织有限公司,河南郑州450007 [2]烟台南山学院,山东烟台265700 [3]东华大学纺织学院,上海201620

出　　处：《上海纺织科技》2014年第6期7-9,38,共4页Shanghai Textile Science & Technology

基　　金：国家自然科学基金项目(51173023)

摘　　要：采用WEKA特征选择法分析了原棉的性能指标对成纱质量的影响,并结合BP人工神经网络对成纱条干、成纱强度和成纱强度不匀进行了预测。采用WEKA特征选择法可以减少BP神经网络的输入节点数,与单纯的BP神经网络的预测结果相比,WEKA特征选择法结合BP神经网络预测结果较准确,预测值与实测值之间的平均相对误差较小。In this paper, the effect of cotton fiber property on formed yam quality is analyzed by WEKA feature selection. Combined with BP neural network, formed yarn evenness, strength and strength irregularity are predicted. The WEKA feature selection is expected to reduce the input layer node numbers of BP neural network. Compared with that of pure BP neural network, the result got from the combined one is more precise; the relative mean error between the measured values and the forcast results of cotton yarn quality is reduced.

关 键 词：原棉 性能 成纱质量 BP神经网络 WEKA特征选择 

分 类 号：TS111.8[轻工技术与工程—纺织材料与纺织品设计]